Ecological Model Selection via Evolutionary Computation and Information Theory

Abstract

This paper describes an evolutionary algorithm-based approach to model selection and demonstrates its effectiveness in using the information content of ecological data to choose the correct model structure. Experiments with a modified genetic algorithm are described that combine parsimony with a novel gene regulation mechanism. This combination creates evolvable switches that implement functional variable-length genomes in the GA that allow for simultaneous model selection and parameter fitting. In effect, the GA orchestrates a competition among a community of models. Parsimony is implemented via the Akaike Information Criterion, and gene regulation uses a modulo function to overload the gene values and create an evolvable binary switch. The approach is shown to successfully specify the correct model structure in experiments with a nested set of polynomial test models and complex biological simulation models, even when Gaussian noise is added to the data.